Research map / Learning theory and algorithms / Clustering
PIVE: Per-Iteration Visualization Environment for Real-Time Interactions with Dimension Reduction and Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| hard-freeze three stable k-means clusters at iteration 6 | k-means in Jigsaw · CHI papers 1999-2010 | final cluster membership agreement: less than 10% of final memberships differ | No clear effect |
| hard-freeze three stable k-means clusters at iteration 6 | k-means in Jigsaw · CHI papers 1999-2010 | computing time of subsequent iterations | Helped |
| use intermediate outputs early instead of waiting for all iterations | MDS · 500 handwritten digits | iterations needed for stable visualization: 20 of 93 iterations | Helped |
| use intermediate outputs early instead of waiting for all iterations | t-SNE · 1,558 spoken letters | iterations needed for stable visualization: 130th of 1,000 iterations | Helped |
| use intermediate outputs early instead of waiting for all iterations | LDA · 20 newsgroups | visual stability of cluster membership | Hurt |
| filter noisy documents mid-run with PIVE interaction | LDA in iVisClustering · 20 newsgroups | topic quality / time: around 700th iteration vs two full sets of 1,000 | Helped |
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